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Starts 4 July 2025 16:49

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Neural Scaling for Small LMs and AI Agents - How Superposition Yields Robust Neural Scaling

Join this insightful event to explore the mechanisms behind neural scaling for small language models (LMs) and AI agents, focusing on how superposition contributes to robust and efficient information representation. Discover the reasons behind the enhanced capabilities of larger foundation models, which follow predictable power-law decay patt.
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Overview

Join this insightful event to explore the mechanisms behind neural scaling for small language models (LMs) and AI agents, focusing on how superposition contributes to robust and efficient information representation. Discover the reasons behind the enhanced capabilities of larger foundation models, which follow predictable power-law decay patterns, leading to improved performance and efficiency.

This course is offered by YouTube and belongs to the categories of Artificial Intelligence Courses and Computer Science Courses.

Perfect for professionals and enthusiasts keen on understanding the dynamic scaling of neural networks and the pivotal role of superposition in AI development.

Syllabus

  • Introduction to Neural Scaling
  • Overview of neural scaling laws
    Historical context and development
    Importance in AI research and applications
  • Foundations of Superposition in Neural Networks
  • Definition and concept of superposition
    Mathematical formulations and principles
    Role of superposition in neural networks
  • Power-Law Patterns in AI Models
  • Explanation of power-law decay
    Interpretations in the context of AI
    Empirical evidence and case studies
  • Scaling Behaviors in Small Language Models (LMs)
  • Characteristics of small-scale language models
    Benefits and limitations compared to large models
    Case studies and applications
  • Robust Neural Scaling via Superposition
  • Mechanisms of robust scaling
    Superposition's contribution to scalability
    Comparative analysis with non-superpositional methods
  • Practical Implications for AI Agents
  • Implementation in AI agents and real-world systems
    Performance improvements and efficiency gains
    Challenges and potential solutions
  • Case Studies and Applications
  • Real-world examples of neural scaling in action
    Industry applications of small LMs and AI agents
    Future directions and ongoing research
  • Conclusion and Future Perspectives
  • Summary of key concepts
    Emerging trends in neural scaling and AI
    Open questions and research opportunities
  • Supplementary Materials
  • Recommended readings and resources
    Online tools and datasets for further exploration

Subjects

Computer Science